vLLM: Derender endpoints decode caller-supplied GenerateResponse token IDs without output bounds

Summary

The /v1/completions/derender and /v1/chat/completions/derender endpoints accept caller-supplied GenerateResponse objects and postprocess every nested choices[*].token_ids list directly. Unlike the normal render/generate path, derender does not enforce model context length, resolved max_tokens, max_num_seqs, choice-count, or response-size bounds before detokenizing and returning the supplied token IDs. An authenticated API client can therefore make the CPU-only render frontend, or any server exposing these /v1 derender routes, spend CPU and memory proportional to attacker-chosen generated-output-shaped JSON rather than to a bounded generation result.

Technical Details

The render router registers /v1/chat/completions/derender and /v1/completions/derender in vllm/entrypoints/serve/render/api_router.py, and the OpenAI API server attaches this router whenever "generate" or "render" is in supported_tasks (vllm/entrypoints/openai/api_server.py). The routes are under /v1, so they are part of the OpenAI-compatible HTTP API surface and are protected by the API-key middleware when --api-key is configured.

The request types trust generated-output-shaped data from the client. In vllm/entrypoints/serve/disagg/protocol.py, GenerateResponseChoice accepts token_ids: list[int] | None = None, GenerateResponse accepts choices: list[GenerateResponseChoice], and DerenderCompletionRequest accepts generate_responses: list[GenerateResponse]. These fields have no max length, max item count, or relationship to a prior GenerateRequest.

The sink is OnlineDerenderer. derender_completion() iterates every supplied generate_responses entry and every nested choice, calls tokenizer.decode(choice.token_ids, skip_special_tokens=True), appends the decoded text to the response choices, and increments total_completion_tokens from the same supplied list length. derender_chat() has the same shape for a single supplied generate_response, and can also feed the decoded text into tool/reasoning parsers when a parser and chat_request are present. ServingRender.derender_completion_response() calls online_derenderer.derender_completion(request.generate_responses, request.prompt_tokens) before applying any completion-level validation beyond the model check.

Normal render and generation paths derive output limits from max_model_len, the rendered prompt length, request max_tokens / max_completion_tokens, and scheduler limits. Derender bypasses that invariant because it accepts the already-generated output shape directly from the HTTP caller. The missing invariant is: derender should only postprocess bounded generated output, and client-supplied derender payloads must be rejected if their nested generated token/logprob structures exceed the same limits that generation would have enforced.

PoV

The following bounded PoV can be run from a current vLLM checkout containing PR #43606. It asserts the current source facts for the derender routes, unchecked request fields, and decode sink, then simulates the same derender loop with a counting tokenizer. The negative control is a one-choice, 32-token response. The amplified payload keeps the test bounded but demonstrates that all decoded work and returned text scale directly with caller-supplied GenerateResponse contents.

#!/usr/bin/env python3
import subprocess
from dataclasses import dataclass
from pathlib import Path

SOURCE = Path(".")

def require_source_fact(path: str, needles: list[str]) -> None:
    text = (SOURCE / path).read_text()
    missing = [needle for needle in needles if needle not in text]
    if missing:
        raise AssertionError(f"{path} missing expected facts: {missing}")

def source_head() -> str:
    return subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=SOURCE, text=True).strip()

@dataclass
class Choice:
    index: int
    token_ids: list[int]

@dataclass
class GenerateResponse:
    request_id: str
    choices: list[Choice]

class CountingTokenizer:
    def __init__(self) -> None:
        self.decode_calls = 0
        self.decoded_ids = 0
    def decode(self, token_ids: list[int], *, skip_special_tokens: bool = True) -> str:
        self.decode_calls += 1
        self.decoded_ids += len(token_ids)
        return "x" * len(token_ids)

def derender_completion_like_current_head(generate_responses: list[GenerateResponse], tokenizer: CountingTokenizer) -> tuple[int, int, int]:
    output_chars = 0
    choices = 0
    total_completion_tokens = 0
    for gen in generate_responses:
        for choice in gen.choices:
            if not choice.token_ids:
                raise ValueError("choice has empty or null token_ids")
            decoded_text = tokenizer.decode(choice.token_ids, skip_special_tokens=True)
            output_chars += len(decoded_text)
            total_completion_tokens += len(choice.token_ids)
            choices += 1
    return choices, total_completion_tokens, output_chars

def make_payload(responses: int, choices_per_response: int, tokens_per_choice: int) -> list[GenerateResponse]:
    token_ids = [42] * tokens_per_choice
    return [GenerateResponse(request_id=f"gen-{r}", choices=[Choice(index=c, token_ids=list(token_ids)) for c in range(choices_per_response)]) for r in range(responses)]

def run_case(name: str, payload: list[GenerateResponse]) -> None:
    tokenizer = CountingTokenizer()
    choices, completion_tokens, output_chars = derender_completion_like_current_head(payload, tokenizer)
    print(f"{name}: responses={len(payload)} choices={choices} decode_calls={tokenizer.decode_calls} decoded_token_ids={tokenizer.decoded_ids} completion_tokens={completion_tokens} output_chars={output_chars}")

require_source_fact("vllm/entrypoints/serve/render/api_router.py", ['"/v1/completions/derender"', '"/v1/chat/completions/derender"', "app.include_router(router)"])
require_source_fact("vllm/entrypoints/serve/disagg/protocol.py", ["class GenerateResponseChoice(BaseModel):", "token_ids: list[int] | None = None", "class GenerateResponse(BaseModel):", "choices: list[GenerateResponseChoice]", "class DerenderCompletionRequest(BaseModel):", "generate_responses: list[GenerateResponse]"])
require_source_fact("vllm/renderers/online_derenderer.py", ["async def derender_completion(", "for gen, pt in zip(generate_responses, prompt_tokens_list):", "for choice in gen.choices:", "decoded_text = tokenizer.decode(", "total_completion_tokens += len(choice.token_ids)"])
print("source_checks=ok")
print(f"source_head={source_head()}")
run_case("negative_control", make_payload(responses=1, choices_per_response=1, tokens_per_choice=32))
run_case("amplified_payload", make_payload(responses=16, choices_per_response=4, tokens_per_choice=8192))
print("observation=derender decodes every caller-supplied token id before any max_model_len, max_tokens, max_num_seqs, or response-size check")

Impact

An attacker with access to the /v1 API can send derender requests that consume CPU and memory in the frontend/postprocessing process and can cause large responses unrelated to any bounded generation. In disaggregated deployments, this affects the CPU-only render frontend; in servers where the render router is attached alongside generation, it affects the same OpenAI-compatible server process that handles normal client traffic. This can degrade availability for other clients sharing the process.

Likely CWE: CWE-400 (Uncontrolled Resource Consumption) / CWE-770 (Allocation of Resources Without Limits or Throttling). Conservative CVSS v3.1: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L (4.3). This is not Low severity because a regular network API client can induce availability impact in a shared service without local access, invalid model artifacts, or special runtime privileges. If the server is deployed without API-key enforcement for /v1, the privileges component becomes PR:N.

Suggested Fix

Validate derender payloads before any detokenization or parser invocation. Apply bounded limits to generate_response(s), choices, token_ids, prompt_logprobs, logprobs.content, top_logprobs, and routed_experts that are at least as strict as the corresponding generation-side limits. For completions, reject generate_responses counts above the number of prompts that /v1/completions/render would have produced, and reject total nested choice counts above max_num_seqs / n limits. For each choice, reject token_ids longer than the resolved output-token budget, or require derender callers to submit the original bounded GenerateRequest / sampling metadata and validate the GenerateResponse against it before decoding.

Add regression tests for both derender endpoints. The tests should show that a normal bounded derender payload succeeds, while oversized generate_responses, oversized choices, oversized token_ids, and oversized logprob/top-logprob structures are rejected before tokenizer.decode() or parser execution.

Affected Package/Versions

Confirmed affected: current main at ddd3855a28a561a5bb54d380c6e6b8b1e883cc4a and downstream/nightly builds that include the derender endpoints introduced by PR #43606. The derender router, request models, decode sink, render serving bridge, and OpenAI API router attachment have no relevant diff from 00e045b7c7b82599f626779e111233abd4d0a64e to ddd3855a28a561a5bb54d380c6e6b8b1e883cc4a.

Latest release checked: v0.23.0, published on 2026-06-15. Its vllm/entrypoints/serve/render/api_router.py does not expose /v1/completions/derender or /v1/chat/completions/derender, so v0.23.0 was not confirmed affected.

Advisory History

PR #43606 ("[Render] Add /derender endpoints for disaggregated postprocessing") introduced the derender endpoints on main. PR #44285 later refactored the render serving code, and current head still contains the unchecked derender flow.

Public issue search for derender GenerateResponse token_ids returned no reports. Public search for "/v1/completions/derender" returned the derender feature RFC #42729 and unrelated bugs, but no size-bound, DoS, or generated-output postprocessing issue.

Related public request-fanout and resource-bound advisories are distinct:

• GHSA-3mwp-wvh9-7528 covers an unbounded n parameter on the normal OpenAI completion/chat generation routes. Its root cause is missing upper-bound validation for generated sequence count, its sink is request fanout and request-object copying into the async engine path before scheduling, its precondition is a caller-controlled n, and its fix surface is a cap on generated sequence count. This report reaches /v1/completions/derender and /v1/chat/completions/derender, not the normal generate routes; its root cause is unchecked caller-supplied GenerateResponse / choices / token_ids structures, its sink is OnlineDerenderer detokenization and response construction after generation, its precondition is access to the derender API with generated-output-shaped JSON, and its fix surface is derender payload validation before decode.
• PR #45390 includes the GHSA-83mh-6mwq-3hg9 batch-message fanout fix class: it bounds the outer BatchChatCompletionRequest.messages conversation list to prevent one request from creating many conversation/request objects before normal generation. This report has no batch conversation list and does not rely on n; one derender request can instead supply oversized nested GenerateResponse choices and token IDs that are detokenized and returned directly. A batch-message max_length limit would not bound derender generate_response(s) or per-choice token/logprob structures.

The completed local report titled "Explicit truncation_side disables tokenizer-level prompt truncation" is also distinct. That report used /v1/completions and /v1/chat/completions with ordinary prompt text plus truncate_prompt_tokens and explicit truncation_side; its root cause was the renderer omitting tokenizer-level max_length and the pre-tokenization character guard before post-token slicing; its sink was prompt tokenization; and its fix surface was preserving tokenizer-level truncation or rejecting over-budget prompts before tokenization. This derender report uses /v1 derender routes, has no prompt text tokenization or truncation-side control, starts from caller-supplied generated-output token IDs, and needs aggregate bounds on derender generate_response(s), choices, token IDs, logprobs, parser inputs, and response construction before detokenization.

Other adjacent vLLM advisories for Rust/gRPC token-id and logprob bounds, structured-output grammar amplification, repetition-detection windows, and pooling/rerank batch fanout are distinct. Those issues affect Rust/gRPC request conversion, grammar compilation, scheduler loops, or engine fanout. This issue affects /v1 derender postprocessing of caller-supplied generated-output objects and requires derender-specific request validation before detokenization.

Resources

• vllm/entrypoints/serve/render/api_router.py
• vllm/entrypoints/serve/disagg/protocol.py
• vllm/renderers/online_derenderer.py
• vllm/entrypoints/serve/render/serving.py
• vllm/entrypoints/openai/api_server.py
• PR #43606: https://github.com/vllm-project/vllm/pull/43606
• PR #44285: https://github.com/vllm-project/vllm/pull/44285
• GHSA-3mwp-wvh9-7528: https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528
• PR #45390: https://github.com/vllm-project/vllm/pull/45390
• Release v0.23.0: https://github.com/vllm-project/vllm/releases/tag/v0.23.0